Article | Proceedings of SIGRAD 2013; Visual Computing; June 13-14; 2013; Norrköping; Sweden | Feature Tracking in Time-Varying Volumetric Data through Scale Invariant Feature Transform Link�ping University Electronic Press Conference Proceedings
Göm menyn

Title:
Feature Tracking in Time-Varying Volumetric Data through Scale Invariant Feature Transform
Author:
Khoa Tan Nguyen: Scientific Visualization Group, Linköping University, Sweden Timo Ropinski: Scientific Visualization Group, Linköping University, Sweden
Download:
Full text (pdf)
Year:
2013
Conference:
Proceedings of SIGRAD 2013; Visual Computing; June 13-14; 2013; Norrköping; Sweden
Issue:
094
Article no.:
002
Pages:
11-16
No. of pages:
6
Publication type:
Abstract and Fulltext
Published:
2013-11-04
ISBN:
978-91-7519-455-4
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press; Linköpings universitet


Export in BibTex, RIS or text

Recent advances in medical imaging technology enable dynamic acquisitions of objects under movement. The acquired dynamic data has shown to be useful in different application scenarios. However; the vast amount of timevarying data put a great demand on robust and efficient algorithms for extracting and interpreting the underlying information. In this paper; we present a gpu-based approach for feature tracking in time-varying volumetric data set based on the Scale Invariant Feature Transform (SIFT) algorithm. Besides; the improved performance; this enables us to robustly and efficiently track features of interest in the volumetric data over the time domain. As a result; the proposed approach can serve as a foundation for more advanced analysis on the features of interest in dynamic data sets. We demonstrate our approach using a time-varying data set for the analysis of internal motion of breathing lungs.

Proceedings of SIGRAD 2013; Visual Computing; June 13-14; 2013; Norrköping; Sweden

Author:
Khoa Tan Nguyen, Timo Ropinski
Title:
Feature Tracking in Time-Varying Volumetric Data through Scale Invariant Feature Transform
References:

[AKB?08] ALLAIRE S.; KIM J. J.; BREEN S. L.; JAFFRAY D. A.; PEKAR V.: Full orientation invariance and improved feature selectivity of 3D SIFT with application to medical image analysis. In Computer Vision and Pattern Recognition Workshops; 2008. CVPRW’08. IEEE Computer Society Conference on (2008); IEEE; pp. 1–8. 2; 3

[BL97] BEIS J.; LOWE D.: Shape indexing using approximate nearest-neighbour search in high-dimensional spaces. In Computer Vision and Pattern Recognition; 1997. Proceedings.; 1997 IEEE Computer Society Conference on (1997); pp. 1000–1006. 3

[CCG?09] CASTILLO R.; CASTILLO E.; GUERRA R.; JOHNSON V. E.; MCPHAIL T.; GARG A. K.; GUERRERO T.: A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets. Physics in Medicine and Biology 54; 7 (2009); 1849. 4; 5

[CCZG09] CASTILLO E.; CASTILLO R.; ZHANG Y.; GUERRERO T.: Compressible image registration for thoracic computed tomography images. Journal of Medical and Biological Engineering 29; 5 (2009); 222–233. 4; 5

[CH07] CHEUNG W.; HAMARNEH G.: n-SIFT: N-dimensional scale invariant feature transform for matching medical images. In Biomedical Imaging: From Nano to Macro; 2007. ISBI 2007. 4th IEEE International Symposium on (2007); IEEE; pp. 720–723. 2

[CH09] CHEUNG W.; HAMARNEH G.: n-SIFT: n-dimensional scale invariant feature transform. IEEE Transactions on Image Processing 18; 9 (2009); 2012–2021. 2

[FB81] FISCHLER M. A.; BOLLES R. C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24; 6 (1981); 381–395. 3

[FBMB] FLITTON G.; BRECKON T.; MEGHERBI BOUALLAGU N.: Object Recognition using 3D SIFT in Complex CT Volumes. In British Machine Vision Conference 2010; British Machine Vision Association; pp. 11.1–11.12. 2

[HMS?07] HEYMANN S.; MULLER K.; SMOLIC A.; FROHLICH B.; WIEGAND T.: Sift implementation and optimization for general-purpose gpu. In Proceedings of the international conference in Central Europe on computer graphics; visualization and computer vision (2007); p. 144. 2; 3

[Lin94] LINDEBERG T.: Scale-space theory: A basic tool for analyzing structures at different scales. Journal of applied statistics 21; 1-2 (1994); 225–270. 2

[Low99] LOWE D. G.: Object recognition from local scaleinvariant features. Computer Vision; 1999; The Proceedings of the Seventh IEEE International Conference on 2 (1999); 1150–1157. 1

[Low04] LOWE D. G.: Distinctive image features from scaleinvariant keypoints. International Journal of Computer Vision 60; 2 (2004); 91–110. 1; 2; 3; 4

[MTS?05] MIKOLAJCZYK K.; TUYTELAARS T.; SCHMID C.; ZISSERMAN A.; MATAS J.; SCHAFFALITZKY F.; KADIR T.; GOOL L. V.: A comparison of affine region detectors. International journal of computer vision 65; 1 (2005); 43–72. 2

[MW47] MANN H. B.; WHITNEY D. R.: On a test of whether one of two random variables is stochastically larger than the other. The annals of mathematical statistics 18; 1 (1947); 50–60. 4

[NQY?08] NI D.; QU Y.; YANG X.; CHUI Y. P.; WONG T.-T.; HO S. S.; HENG P. A.: Volumetric Ultrasound Panorama Based on 3D SIFT. In MICCAI ’08: Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention; Part II (Sept. 2008); Springer-Verlag. 2

[PPP?12] PAGANELLI C.; PERONI M.; PENNATI F.; BARONI G.; SUMMERS P.; BELLOMI M.; RIBOLDI M.: Scale invariant feature transform as feature tracking method in 4d imaging: A feasibility study. In Engineering in Medicine and Biology Society (EMBC); 2012 Annual International Conference of the IEEE (2012); pp. 6543–6546. 2; 4; 5

[SAS07] SCOVANNER P.; ALI S.; SHAH M.: A 3-dimensional SIFT descriptor and its application to action recognition. In Proceedings of the 15th international conference on Multimedia (2007); ACM; pp. 357–360. 2

[TWICA10] TOEWS M.; WELLS III W.; COLLINS D. L.; ARBEL T.: Feature-based morphometry: Discovering group-related anatomical patterns. NeuroImage 49; 3 (2010); 2318–2327. 2

[YWC12] YU T.-H.; WOODFORD O. J.; CIPOLLA R.: A Performance Evaluation of Volumetric 3D Interest Point Detectors. International Journal of Computer Vision 102; 1-3 (Sept. 2012); 180–197. 2

Proceedings of SIGRAD 2013; Visual Computing; June 13-14; 2013; Norrköping; Sweden

Author:
Khoa Tan Nguyen, Timo Ropinski
Title:
Feature Tracking in Time-Varying Volumetric Data through Scale Invariant Feature Transform
Note: the following are taken directly from CrossRef
Citations:
No citations available at the moment


Responsible for this page: Peter Berkesand
Last updated: 2018-9-11